2018_SH1_aliphatic_thermalmaturity
Start up functions
Read and organize data
read_srm_export <- function(filename, columns = c("peak_name", "RT.min", "basepeak", "area.cpm", "height.cts", "quantitation")) {
filename %>%
# read excel files
read_excel(sheet = "Integration", skip = 42,
col_names = columns, col_types = rep("text", length(columns))) %>%
as_data_frame() %>%
# remove empty rows
filter(!is.na(peak_name), peak_name != "n.a.") %>%
# convert the relevant numeric columns into numbers
mutate_at(vars(RT.min, area.cpm, height.cts), as.numeric) %>%
# remove useless columns
select(-basepeak, -quantitation) %>%
# add filename info
mutate(file_id = gsub("\\.xls", "", basename(filename))) %>%
select(file_id, everything())
}
# get data
all_data <-
# find all excel files ##change name and use new folder for new project
list.files("data_SH1", recursive = TRUE, full.names = TRUE, pattern = "\\.xls$") %>%
# send them to the read method
lapply(read_srm_export) %>%
# combine the data set
bind_rows() %>%
# pull out sample information
#mutate(sample_id = str_match(all_data$file_id, "TSQ\\d+_GB_(.*)$") %>% { .[,2] }) %>%
# get n replicates
group_by(file_id)
#mutate(n_replicates = length(unique(file_id)))File names for metadata file
Calculation peak amounts and rock concentrations, ID standards
depth_and_rock_info <- read_excel(file.path("metadata", "aliphaticSRM_SH1.xlsx")) %>%
rename(tle = `TLE.mg`, maltene = `maltenes.mg`, ref_amount_added.ug = `D4.ug` )%>%
filter(!is.na(file_id))%>%
filter(process == "yes")
depth_and_rock_info## # A tibble: 69 x 8
## file_id OG depth rock.g tle maltene ref_amount_adde… process
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 TSQ3481_G… 2.00 125 10.0 24.9 13.7 100 yes
## 2 TSQ3482_G… 3.00 124 10.1 224 7.30 100 yes
## 3 TSQ3483_G… 4.00 123 10.3 - 14.4 5.00 100 yes
## 4 TSQ3484_G… 5.00 122 9.28 3.20 5.40 100 yes
## 5 TSQ3485_G… 6.00 121 10.6 33.4 NA 100 yes
## 6 TSQ3486_G… 7.00 119 9.62 -214 4.80 100 yes
## 7 TSQ3489_G… 8.00 118 9.70 3.40 5.00 100 yes
## 8 TSQ3490_G… 9.00 117 10.7 5.00 6.30 100 yes
## 9 TSQ3491_G… 10.0 115 11.1 5.00 6.00 100 yes
## 10 TSQ3492_G… 11.0 114 10.2 0.400 6.90 100 yes
## # ... with 59 more rowsdata_by_depth <-
all_data %>%
left_join(depth_and_rock_info, by = "file_id") %>%
group_by(file_id) %>%
mutate(
n_peaks = n(),
n_standards = sum(peak_name == "D4 C29 ISTD"),
ref_area.cpm = area.cpm[peak_name == "D4 C29 ISTD"],
amount.ug = area.cpm/ref_area.cpm * ref_amount_added.ug,
#Normalize by what you want
conc_rock.ug_g = amount.ug / rock.g,
conc_tle.ug.g = amount.ug / tle,
conc_maltene.ug.g = amount.ug / maltene
)%>% ungroup() %>%
arrange(file_id, peak_name)
data_by_depth## # A tibble: 11,665 x 19
## file_id peak_name RT.min area.cpm height.cts OG depth rock.g tle
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TSQ3466… 20R 4a,24… 34.7 42909 396389 22.0 102 11.0 4.50
## 2 TSQ3466… 20R, 4a M… 33.4 33439 425331 22.0 102 11.0 4.50
## 3 TSQ3466… 20R, 4a M… 32.8 49529 763046 22.0 102 11.0 4.50
## 4 TSQ3466… 20R, 4a,2… 35.3 26212 404760 22.0 102 11.0 4.50
## 5 TSQ3466… 20S, 4a M… 32.1 49207 882869 22.0 102 11.0 4.50
## 6 TSQ3466… 20S, 4a,2… 35.8 31831 268610 22.0 102 11.0 4.50
## 7 TSQ3466… 20S, 4a,2… 34.0 51613 456842 22.0 102 11.0 4.50
## 8 TSQ3466… 25-nor C2… 37.6 11915 155542 22.0 102 11.0 4.50
## 9 TSQ3466… 28, 30 C2… 37.0 27403 343070 22.0 102 11.0 4.50
## 10 TSQ3466… 29, 30 C2… 35.8 5780 63220 22.0 102 11.0 4.50
## # ... with 11,655 more rows, and 10 more variables: maltene <dbl>,
## # ref_amount_added.ug <dbl>, process <chr>, n_peaks <int>,
## # n_standards <int>, ref_area.cpm <dbl>, amount.ug <dbl>,
## # conc_rock.ug_g <dbl>, conc_tle.ug.g <dbl>, conc_maltene.ug.g <dbl>Calculate Recovery
Linear regressions of the calibration curves
standard <- read_excel(file.path("metadata", "D4_calibration.xlsx")) ###read excel
###calibration curve
standard %>%
ggplot() +
aes(x = Known.ng, y = Measured_area.counts, color = calibration) +
geom_smooth(method = "lm", alpha = 0.5) +
geom_point() +
theme_bw() +
theme(legend.position = "none") 
calibrations <-
standard %>%
filter(!is.na(calibration)) %>%
nest(-calibration) %>%
mutate(
fit = map(data, ~summary(lm(`Measured_area.counts`~ `Known.ng`, data = .x))),
coefficients = map(fit, "coefficients"),
intercept = map_dbl(coefficients, `[`, 1, 1),
intercept_se = map_dbl(coefficients, `[`, 1, 2),
slope = map_dbl(coefficients, `[`, 2, 1),
slope_se = map_dbl(coefficients, `[`, 2, 2),
r2 = map_dbl(fit, "r.squared")
)
calibrations %>% select(-data, -fit, -coefficients) %>% knitr::kable(d = 3)| calibration | intercept | intercept_se | slope | slope_se | r2 |
|---|---|---|---|---|---|
| jan2018 | 705.862 | 1371.146 | 72929.67 | 3337.256 | 0.996 |
Determine yield
These numbers are not useful for anything else.
calib_data <-
data_by_depth %>%
# temp
mutate(calibration = "jan2018") %>%
left_join(calibrations, by = "calibration") %>%
mutate(
total_volume.uL = 100,
total_inject.uL = 1.5,
ref_amount_inject_expected.ng = (ref_amount_added.ug * 1000)/total_volume.uL * total_inject.uL ,
ref_amount_inject_measured.ng = (ref_area.cpm - intercept)/slope,
ref_amount_measured.ug = ((total_volume.uL* ref_amount_inject_measured.ng)/total_inject.uL) * 1/1000,
yield = (ref_amount_inject_measured.ng/ref_amount_inject_expected.ng) * 100
) %>%
filter(area.cpm > 700)
calib_data## # A tibble: 7,614 x 34
## file_id peak_name RT.min area.cpm height.cts OG depth rock.g tle
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TSQ3466… 20R 4a,24… 34.7 42909 396389 22.0 102 11.0 4.50
## 2 TSQ3466… 20R, 4a M… 33.4 33439 425331 22.0 102 11.0 4.50
## 3 TSQ3466… 20R, 4a M… 32.8 49529 763046 22.0 102 11.0 4.50
## 4 TSQ3466… 20R, 4a,2… 35.3 26212 404760 22.0 102 11.0 4.50
## 5 TSQ3466… 20S, 4a M… 32.1 49207 882869 22.0 102 11.0 4.50
## 6 TSQ3466… 20S, 4a,2… 35.8 31831 268610 22.0 102 11.0 4.50
## 7 TSQ3466… 20S, 4a,2… 34.0 51613 456842 22.0 102 11.0 4.50
## 8 TSQ3466… 25-nor C2… 37.6 11915 155542 22.0 102 11.0 4.50
## 9 TSQ3466… 28, 30 C2… 37.0 27403 343070 22.0 102 11.0 4.50
## 10 TSQ3466… 29, 30 C2… 35.8 5780 63220 22.0 102 11.0 4.50
## # ... with 7,604 more rows, and 25 more variables: maltene <dbl>,
## # ref_amount_added.ug <dbl>, process <chr>, n_peaks <int>,
## # n_standards <int>, ref_area.cpm <dbl>, amount.ug <dbl>,
## # conc_rock.ug_g <dbl>, conc_tle.ug.g <dbl>, conc_maltene.ug.g <dbl>,
## # calibration <chr>, data <list>, fit <list>, coefficients <list>,
## # intercept <dbl>, intercept_se <dbl>, slope <dbl>, slope_se <dbl>,
## # r2 <dbl>, total_volume.uL <dbl>, total_inject.uL <dbl>,
## # ref_amount_inject_expected.ng <dbl>,
## # ref_amount_inject_measured.ng <dbl>, ref_amount_measured.ug <dbl>,
## # yield <dbl>check yields
calib_data %>%
select(file_id, peak_name, yield) %>%
arrange(file_id) %>%
unique() %>%
ggplot() + aes(file_id, y = yield) +
geom_point(size = 3) +
theme_bw() + theme(axis.text.x = element_text(angle = 90, hjust = 0, vjust = 0.5))## Warning: Removed 434 rows containing missing values (geom_point).
Combine compounds/make ratios (new rows w/o RTs etc, just concentration.rock column)
# functions to make it easy to sum up peaks
sum_peaks <- function(df, filter_condition, new_peak_name) {
filter_condition <- sprintf("(%s)", str_c(filter_condition, collapse = "|"))
filter(df, str_detect(peak_name, filter_condition)) %>%
summarize(
file_id = file_id[1],
depth = depth[1],
conc_rock.ug_g = sum(conc_rock.ug_g)
) %>%
mutate(peak_name = new_peak_name)
}
ratio_peaks <- function(df, filter_top, filter_bottom, new_peak_name) {
filter_top <- sprintf("(%s)", str_c(filter_top, collapse = "|"))
filter_bottom <- sprintf("(%s)", str_c(filter_bottom, collapse = "|"))
filter(df, str_detect(peak_name, filter_top) | str_detect(peak_name, filter_bottom)) %>%
summarize(
file_id = file_id[1],
depth = depth[1],
ratio = sum(conc_rock.ug_g[str_detect(peak_name, filter_top)]) / sum(conc_rock.ug_g[str_detect(peak_name, filter_bottom)])
) %>%
mutate(peak_name = new_peak_name)
}Ideas for other ratios? What about dimethyls?
#set values to use for later calculations
final_data1 <- calib_data %>%
group_by(file_id) %>%
do({
bind_rows(.,
#C27_Dia/Reg
sum_peaks(., c("C27 aB 20R ST", "C27 aB 20S ST"), "C27Dia"),
sum_peaks(., c("C27 aaa 20R ST", "C27 aaa 20S ST", "C27 aBB 20R ST", "C27 aBB 20S ST", "C27 Ba 20R ST", "C27 Ba 20S ST"), "C27Reg"),
#Total Tricyclics
sum_peaks(., c("C19 Tri HO", "C20 Tri HO", "C21 Tri HO", "C22 Tri HO", "C23 Tri HO", "C24 Tet HO", "C24 Tri HO", "C25 Tri R HO", "C25 Tri S HO", "C26 Tri R HO", "C26 Tri S HO"), "all_tricyclics"),
#4Me_TriMe
sum_peaks(., c("4B Me 5a cholestane", "4B Me 24 ethyl 5a cholestane", "4B,23S,24S trimethyl 5a cholestane", "4B,23S,24R trimethyl 5a cholestane", "4B,23R,24S trimethyl 5a cholestane", "4B,23R,24R trimethyl 5a cholestane", "4a Me 5a cholestane", "4a Me 24 ethyl 5a cholestane", "4a,23S,24S trimethyl 5a cholestane", "4a,23S,24R trimethyl 5a cholestane", "4a,23R,24S trimethyl 5a cholestane", "4a,23R,24R trimethyl 5a cholestane"), "4Me_TriMe"),
#allRegSt
sum_peaks(., c("C26 aBB 20S ST", "C26 aBB 20R ST", "C26 aaa 20S ST", "C26 aaa 20R ST","C27 aBB 20S ST", "C27 aBB 20R ST", "C27 aaa 20S ST", "C27 aaa 20R ST", "C28 aBB 20S ST", "C28 aBB 20R ST", "C28 aaa 20S ST", "C28 aaa 20R ST","C29 aBB 20S ST", "C29 aBB 20R ST", "C29 aaa 20S ST", "C29 aaa 20R ST", "C30 aBB 20 R+S ST", "C30 aaa 20S ST", "C30 aaa 20R ST"), "allRegst"),
#allRegHO
sum_peaks(., c("Ts C27 HO", "Tm C27 HO", "C27 17B H Ho", "29, 30 C28H bisnor HO", "28, 30 C28 bisnor HO", "C29 Ts HO", "C29 Ba HO", "C29 BB Ho", "C30 aB HO", "C30 BB HO", "C30H Ba HO", "C31 HR Ba HO", "C31 aB HR HO", "C31 aB HS HO", "C31 BB HO", "C32 aB HS HO", "C32 aB HR HO", "C33 aB HS HO", "C33 aB HR HO", "C34 aB HR HO", "C34 aB HS HO", "C35 aB HR HO", "C35 aB HS HO"), "allRegHO")
) }) %>% ungroup()final_data <- final_data1 %>%
group_by(file_id) %>%
do({
bind_rows(.,
#Thermal Maturity
#C27_Dia/Reg
ratio_peaks(., "C27Dia", "C27Reg", "C27Dia/Reg"),
#C27Dia_S/R
ratio_peaks(., "C27 aB 20R ST", "C27 aB 20S ST", "C27Dia_S/R"),
#C27Reg_abb/all
ratio_peaks(., c("C27 aBB 20R ST", "C27 aBB 20S ST"), c("C27 aaa 20R ST", "C27 aaa 20S ST", "C27 aBB 20R ST", "C27 aBB 20S ST"), "C27Reg_abb/aaa"),
#C27RegaaaS/S+R
ratio_peaks(., "C27 aaa 20S ST", c("C27 aaa 20R ST", "C27 aaa 20S ST"), "C27Regaaa_S/S+R"),
#C27RegabbS/S+R
ratio_peaks(., "C27 aBB 20S ST", c("C27 aBB 20S ST", "C27 aBB 20R ST"), "C27Regabb_S/S+R"),
#C28Dia/all
ratio_peaks(., c("C28 Ba 20S ST", "C28 Ba 20R ST"), c("C28 aBB 20S ST", "C28 aBB 20R ST", "C28 aaa 20S ST", "C28 aaa 20R ST", "C28 Ba 20S ST", "C28 Ba 20R ST"), "C28Dia/all"),
#C28DiaS/S+R
ratio_peaks(., "C28 Ba 20S ST", c("C28 Ba 20S ST", "C28 Ba 20R ST"), "C28DiaS/S+R"),
#C28abb/all
ratio_peaks(., c("C28 aBB 20S ST", "C28 aBB 20R ST"), c("C28 aBB 20S ST", "C28 aBB 20R ST", "C28 aaa 20S ST", "C28 aaa 20R ST"), "C28abb/all"),
#C28aaaS/S+R
ratio_peaks(., "C28 aaa 20S ST", c("C28 aaa 20S ST", "C28 aaa 20R ST"), "C28aaaS/S+R"),
#C28abbS/S+R
ratio_peaks(., "C28 aBB 20S ST", c("C28 aBB 20S ST", "C28 aBB 20R ST"), "C28abbS/S+R"),
#C29Dia/all
ratio_peaks(., c("C29 Ba 20S ST", "C29 Ba 20R ST"), c("C29 Ba 20S ST", "C29 Ba 20R ST", "C29 aBB 20S ST", "C29 aBB 20R ST", "C29 aaa 20S ST", "C29 aaa 20R ST"), "C29Dia/all"),
#C29DiaS/S+R
ratio_peaks(., "C29 Ba 20S ST", c("C29 Ba 20S ST", "C29 Ba 20R ST"), "C29DiaS/S+R"),
#C29abb/all
ratio_peaks(., c("C29 aBB 20S ST", "C29 aBB 20R ST"), c( "C29 aaa 20S ST", "C29 aaa 20R ST", "C29 aBB 20S ST", "C29 aBB 20R ST" ), "C29abb/all"),
#C29aaaS/S+R
ratio_peaks(., "C29 aaa 20S ST", c("C29 aaa 20S ST", "C29 aaa 20R ST") , "C29aaaS/S+R"),
#C29abbS/S+R
ratio_peaks(., "C29 aBB 20S ST", c("C29 aBB 20S ST", "C29 aBB 20R ST"), "C29abbS/S+R"),
#C27Ts/Ts+Tm
ratio_peaks(., "Ts C27 HO", c("Ts C27 HO", "Tm C27 HO"), "C27Ts/Tm"),
#C28BNH29,30/28,30
ratio_peaks(., "29, 30 C28 bisnor HO", c("29, 30 C28 bisnor HO", "28, 30 C28 bisnor HO"), "C28BNH29,30/28,30"),
#C29Ts/Ts+ab
ratio_peaks(., "C29 Ts HO", c( "C29 aB HO", "C29 Ts HO"), "C29Ts/ab"),
#C29ba/ba+ab
ratio_peaks(.,"C29 Ba HO", c("C29 aB HO", "C29 Ba HO"), "C29ba/ab"),
#C29bb/bb+ab
ratio_peaks(., "C29 BB Ho", c("C29 BB Ho", "C29 aB HO"), "C29bb/ab"),
#C30_30nor/30nor+ab
ratio_peaks(., "30-nor C30H HO", c("C30 aB HO", "30-nor C30H HO"), "C30_30nor/ab"),
#C30ba/ba+ab
ratio_peaks(., "C30H Ba HO", c("C30 aB HO", "C30H Ba HO"), "C30ba/ab"),
#C30bb/bb+ab
ratio_peaks(., "C30 BB HO", c("C30 aB HO", "C30 BB HO"), "C30bb/ab"),
#C31S/S+R
ratio_peaks(., "C31 aB HS HO", c("C31 aB HR HO", "C31 aB HS HO"), "C31S/S+R"),
#C32S/S+R
ratio_peaks(., "C32 aB HS HO", c("C32 aB HS HO", "C32 aB HR HO"), "C32S/S+R"),
#C33S/S+R
ratio_peaks(., "C33 aB HS HO", c("C33 aB HS HO", "C33 aB HR HO"), "C33S/S+R"),
#C34S/S+R
ratio_peaks(., "C34 aB HS HO", c("C34 aB HS HO", "C34 aB HR HO") , "C34S/S+R"),
#C35S/S+R
ratio_peaks(., "C35 aB HS HO", c("C35 aB HS HO", "C35 aB HR HO") , "C35S/S+R")
) }) %>% ungroup()
final_data## # A tibble: 10,396 x 35
## file_id peak_name RT.min area.cpm height.cts OG depth rock.g tle
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TSQ3466… 20R 4a,24… 34.7 42909 396389 22.0 102 11.0 4.50
## 2 TSQ3466… 20R, 4a M… 33.4 33439 425331 22.0 102 11.0 4.50
## 3 TSQ3466… 20R, 4a M… 32.8 49529 763046 22.0 102 11.0 4.50
## 4 TSQ3466… 20R, 4a,2… 35.3 26212 404760 22.0 102 11.0 4.50
## 5 TSQ3466… 20S, 4a M… 32.1 49207 882869 22.0 102 11.0 4.50
## 6 TSQ3466… 20S, 4a,2… 35.8 31831 268610 22.0 102 11.0 4.50
## 7 TSQ3466… 20S, 4a,2… 34.0 51613 456842 22.0 102 11.0 4.50
## 8 TSQ3466… 25-nor C2… 37.6 11915 155542 22.0 102 11.0 4.50
## 9 TSQ3466… 28, 30 C2… 37.0 27403 343070 22.0 102 11.0 4.50
## 10 TSQ3466… 29, 30 C2… 35.8 5780 63220 22.0 102 11.0 4.50
## # ... with 10,386 more rows, and 26 more variables: maltene <dbl>,
## # ref_amount_added.ug <dbl>, process <chr>, n_peaks <int>,
## # n_standards <int>, ref_area.cpm <dbl>, amount.ug <dbl>,
## # conc_rock.ug_g <dbl>, conc_tle.ug.g <dbl>, conc_maltene.ug.g <dbl>,
## # calibration <chr>, data <list>, fit <list>, coefficients <list>,
## # intercept <dbl>, intercept_se <dbl>, slope <dbl>, slope_se <dbl>,
## # r2 <dbl>, total_volume.uL <dbl>, total_inject.uL <dbl>,
## # ref_amount_inject_expected.ng <dbl>,
## # ref_amount_inject_measured.ng <dbl>, ref_amount_measured.ug <dbl>,
## # yield <dbl>, ratio <dbl>Against depth
ratios etc
C27Dia/Reg
dia<- subset(final_data, peak_name== "C27Dia/Reg") %>%
ggplot() +
aes(x = ratio, y = depth) +
geom_point() +
facet_wrap(~peak_name, scales = "free") +
scale_y_reverse()
ggplotly(dia)diareg <- subset(final_data, peak_name %in% c("C27Dia", "C27Reg")) %>%
ggplot() +
aes(x = conc_rock.ug_g, y = depth, color = peak_name) +
geom_point() +
#facet_wrap(~peak_name, scales = "free") +
scale_y_reverse()
ggplotly(diareg)# test checks
outcome <- calib_data %>%
group_by(file_id) %>%
select(file_id, peak_name, depth, area.cpm, conc_rock.ug_g) %>%
do({
bind_rows(.,
ratio_peaks(., "C27 aB 20R ST", "C27 aB 20S ST", "C27Dia_S/R")
)
}) %>%
filter(#!str_detect(file_id, "TSQ3779"),
str_detect(peak_name, "C27Dia_S/R"))
#str_detect(peak_name, "aB"))
outcome## # A tibble: 81 x 6
## # Groups: file_id [70]
## file_id peak_name depth area.cpm conc_rock.ug_g ratio
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 TSQ3466_GB_OG022 C27Dia_S/R 102 NA NA 0.145
## 2 TSQ3467_GB_OG023 C27Dia_S/R 101 NA NA 0.151
## 3 TSQ3468_GB_OG025 C27Dia_S/R 98.9 NA NA 0.170
## 4 TSQ3469_GB_OG026 C27Dia_S/R 98.1 NA NA 0.0327
## 5 TSQ3472_GB_OG027 C27Dia_S/R 96.9 NA NA 0.0330
## 6 TSQ3473_GB_OG019 C27Dia_S/R 105 NA NA 0
## 7 TSQ3474_GB_OG020 C27Dia_S/R 104 NA NA 0.0227
## 8 TSQ3475_GB_OG021 C27Dia_S/R 103 NA NA 0.0448
## 9 TSQ3476_GB_OG018 C27Dia_S/R 106 NA NA 0.0335
## 10 TSQ3481_GB_OG002 C27Dia_S/R 125 NA NA 0.111
## # ... with 71 more rowsDiaS/S+R
diasr <- final_data %>%
filter(depth != c(116.490, 116.900)) %>%
filter(peak_name %in% c("C27Dia_S/R" , "C28DiaS/S+R" , "C29DiaS/S+R")) %>%
ggplot() +
aes(x = ratio, y = depth, color = peak_name) +
geom_point() +
#facet_wrap(~peak_name, scales = "free") +
scale_y_reverse()
ggplotly(diasr)C27Regabb/aaa
twentyseven<- subset(final_data, peak_name== "C27Reg_abb/aaa") %>%
ggplot() +
aes(x = ratio, y = depth) +
geom_point() +
facet_wrap(~peak_name, scales = "free") +
scale_y_reverse()
ggplotly(twentyseven)aaa<- final_data %>%
filter(peak_name %in% c("C27Regaaa_S/S+R", "C28aaaS/S+R", "C29aaaS/S+R")) %>%
ggplot() +
aes(x = ratio, y = depth, color = peak_name) +
geom_point() +
facet_grid(~peak_name, scales = "free") +
scale_y_reverse()
ggplotly(aaa)abb <- subset(final_data, peak_name %in% c("C27Regabb_S/S+R", "C28abbS/S+R", "C29abbS/S+R")) %>%
ggplot() +
aes(x = ratio, y = depth, color = peak_name) +
geom_point() +
#facet_wrap(~peak_name, scales = "free") +
scale_y_reverse()
ggplotly(abb)C28/9Dia/all
Dia <- subset(final_data, peak_name %in% c("C28Dia/all", "C29Dia/all")) %>%
ggplot() +
aes(x = depth, y = ratio, color = peak_name) +
geom_point() +
geom_smooth() +
#facet_wrap(~peak_name, scales = "free") +
scale_x_reverse() +
coord_flip()
ggplotly(Dia)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 36 rows containing non-finite values (stat_smooth).C28/9abb/all
subset(final_data, peak_name %in% c("C28abb/all", "C29abb/all")) %>%
ggplot() +
aes(x = ratio, y = depth, color = peak_name) +
geom_point() +
#facet_wrap(~peak_name, scales = "free") +
scale_y_reverse()## Warning: Removed 36 rows containing missing values (geom_point).
C27Ts/Tm
tstm <- subset(final_data, peak_name== "C27Ts/Tm") %>%
ggplot() +
aes(x = depth, y = ratio) +
geom_point() +
geom_line() +
facet_wrap(~peak_name, scales = "free") +
scale_x_reverse() +
coord_flip()
ggplotly(tstm)C28BNH29,30/28,30
bnh <- subset(final_data, peak_name== "C28BNH29,30/28,30") %>%
ggplot() +
aes(x = ratio, y = depth) +
geom_point() +
facet_wrap(~peak_name, scales = "free") +
scale_y_reverse()
ggplotly(bnh)C29Ts/ab
tsab<- subset(final_data, peak_name== "C29Ts/ab") %>%
ggplot() +
aes(x = depth, y = ratio) +
geom_point() +
geom_line() +
facet_wrap(~peak_name, scales = "free") +
scale_x_reverse() +
coord_flip()
ggplotly(tsab)C29,30ba/ab
baab <- subset(final_data, peak_name %in% c("C29ba/ab", "C30ba/ab")) %>%
ggplot() +
aes(x = ratio, y = depth, color = peak_name) +
geom_point() +
#facet_wrap(~peak_name, scales = "free") +
scale_y_reverse()
ggplotly(baab)C29bb/ab
bbab <- subset(final_data, peak_name %in% c("C29bb/ab", "C30bb/ab")) %>%
ggplot() +
aes(x = ratio, y = depth, color = peak_name) +
geom_point() +
#facet_wrap(~peak_name, scales = "free") +
scale_y_reverse()
ggplotly(bbab)C30_30nor/ab
thirtynor <- subset(final_data, peak_name== "C30_30nor/ab") %>%
ggplot() +
aes(x = ratio, y = depth) +
geom_point() +
facet_wrap(~peak_name, scales = "free") +
scale_y_reverse()
ggplotly(thirtynor)subset(final_data, peak_name %in% c("C30 aB HO", "C30H Ba HO")) %>%
ggplot() +
aes(x = area.cpm, y = depth, color = peak_name) +
geom_point() +
#facet_wrap(~peak_name) +
scale_y_reverse()## Warning: Removed 18 rows containing missing values (geom_point).
All Ho S/R
srho <- final_data %>%
filter(peak_name %in% c("C31S/S+R", "C32S/S+R", "C33S/S+R", "C34S/S+R", "C35S/S+R")) %>%
ggplot() +
aes(x = depth, y = ratio, color = peak_name) +
geom_point() +
geom_line() +
#facet_grid(~peak_name) +
scale_x_reverse() +
coord_flip()
ggplotly(srho)